Matching-adjusted indirect comparison via a polynomial-based non-linear optimization method

To demonstrate the potential of fourth-order polynomials within a non-linear optimization framework for matching-adjusted indirect comparison (MAIC). Simulated individual patient data were reweighted via fourth-order polynomials (polyMAIC) to match aggregate-level data across multiple baseline chara...

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Bibliographic Details
Published in:Journal of comparative effectiveness research 2022-06, Vol.11 (8), p.551-561
Main Authors: Alsop, Jonathan C, Pont, Lawrence O
Format: Article
Language:English
Subjects:
comparative effectiveness research
health technology assessment
matching-adjusted indirect comparison
methodology
statistics
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Summary:To demonstrate the potential of fourth-order polynomials within a non-linear optimization framework for matching-adjusted indirect comparison (MAIC). Simulated individual patient data were reweighted via fourth-order polynomials (polyMAIC) to match aggregate-level data across multiple baseline characteristics. The polyMAIC approach employed pre-specified matching tolerances and maximum allowable weights. Matching performance against aggregate-level targets was assessed, and also compared against the current industry-standard MAIC approach (Signorovitch). The polyMAIC method matched aggregate-level targets within pre-specified tolerances. Effective sample sizes were either similar to or somewhat higher than those obtained from the Signorovitch method. Performance gains from polyMAIC tended to increase as matching complexity increased. PolyMAIC incorporates greater flexibility than the industry-standard MAIC approach and demonstrates matching potential.
ISSN:2042-6305
2042-6313
DOI:10.2217/cer-2021-0266